Recently, memory-augmented neural networks (MANNs) have gained significant attention as a critical solution for few-shot learning (FSL). These networks leverage external memory to store prior knowledge, thereby enhancing classification efficiency. Spin-transfer torque magnetic random access memory (STT-MRAM) is particularly suited for this application due to its compact cell size, excellent data retention, and scalability. In this article, we introduce a STT-MRAM-based near-memory computing (NMC) macro specifically designed for MANNs. Our approach incorporates several key innovations aimed at overcoming challenges in hardware implementation while improving MANN performance as follows: 1) a parallel computing architecture within the NMC to expedite$L1$distance computations; 2) a memory invert coding (MIC) and self-termination write (STW) scheme that reduce write operations and energy consumption, addressing the issues of frequent writes and high write currents during the training phase of MANNs; 3) a dynamic offset-compensation sense amplifier (DOC-SA) and high-throughput switch-capacitor (HTSC) readout scheme to improve read accuracy and throughput, tackling low read margins and limited readout bandwidth; 4) an exploration of MANN architectures validates the reusability of the NMC macro. The optimized matching-networks (MCHnets)-based structure achieves an accuracy exceeding 90% in five-way and eight-way Omniglot classification tasks. Fabricated with a 40-nm CMOS technology, our design achieves classification accuracies of 96.37% for eight-way-five-shot tasks and 93.72% for 16-way-five-shot tasks on the Omniglot dataset utilizing the optimized MCHnet, showcasing an impressive energy efficiency of 6.47 TOPS/W at the basis of 16-bit$L1$distance computing in the classification tasks of MANN.

Memory-augmented neural network (MANN) has gained attention as a pivotal solution for few-shot learning (FSL). Among the candidates for associative memory in MANN accelerators, spin-transfer torque magnetic random-access memory (STT-MRAM) stands out for its compact cell area, long data retention time, and excellent scalability. In this paper, we propose an STT-MRAM near-memory computing (NMC) macro for MANN acceleration. The macro contains following innovations: 1) An array-level parallel computing architecture for L1 distance calculation. 2) A low-area-overhead memory-invert coding technique to reduce write energy consumption. 3) A configurable dynamic offset-compensation sense amplifier (CDOC-SA) to improve classification accuracy. Fabricated in 40nm CMOS process, our macro demonstrates an energy efficiency of 6.47 TOPS/W, achieving the classification accuracy of 98.3% and 93% for 8-way-5-shot tasks and 16-way-5-shot tasks on the Omniglot dataset.

3D cartoon rendering has broad application prospect in games, movies and cartoons. To this end, this paper introduces a cartoon rendering illumination model improved from Phong illumination model, and realizes it with 3ds Max SDK in the form of plug-ins. By discretizing diffuse reflection part and specular reflection part in the Phong illumination model, surfaces of models show different blocks of color, among which there are clear boundaries. In addition, by using linear interpolation between different color blocks, boundaries can be soften. The experimental results show that this cartoon rendering illumination model can not only make a cartoon appearance, but also mix with Phong illumination model, and achieve a unique effect.